import json
import re
from random import choices
from string import ascii_letters, digits
from typing import Dict, List, Sequence, Union

import partial_json_parser
from loguru import logger
from partial_json_parser.core.options import Allow
from pydantic import Field

from aphrodite.common.utils import random_uuid
from aphrodite.endpoints.openai.protocol import (ChatCompletionRequest,
                                                 DeltaFunctionCall,
                                                 DeltaMessage, DeltaToolCall,
                                                 ExtractedToolCallInformation,
                                                 FunctionCall, ToolCall)
from aphrodite.endpoints.openai.tool_parsers.abstract_tool_parser import (
    ToolParser, ToolParserManager)
from aphrodite.endpoints.openai.tool_parsers.utils import (
    extract_intermediate_diff)
from aphrodite.transformers_utils.tokenizer import (AnyTokenizer,
                                                    MistralTokenizer)

ALPHANUMERIC = ascii_letters + digits


class MistralToolCall(ToolCall):
    id: str = Field(
        default_factory=lambda: MistralToolCall.generate_random_id())

    @staticmethod
    def generate_random_id():
        # Mistral Tool Call Ids must be alphanumeric with a maximum length of 9.
        # https://github.com/mistralai/mistral-common/blob/21ee9f6cee3441e9bb1e6ed2d10173f90bd9b94b/src/mistral_common/protocol/instruct/validator.py#L299
        return "".join(choices(ALPHANUMERIC, k=9))


@ToolParserManager.register_module("mistral")
class MistralToolParser(ToolParser):
    """
    Tool call parser for Mistral 7B Instruct v0.3, intended for use with the
    examples/tool_chat_template_mistral.jinja template.
    Used when --enable-auto-tool-choice --tool-call-parser mistral are all set
    """

    def __init__(self, tokenizer: AnyTokenizer):
        super().__init__(tokenizer)
        if not isinstance(self.model_tokenizer, MistralTokenizer):
            logger.info(
                "Non-Mistral tokenizer detected when using a Mistral "
                "model..."
            )
        # initialize properties used for state when parsing tool calls in
        # streaming mode
        self.prev_tool_call_arr: List[Dict] = []
        self.current_tool_id: int = -1
        self.current_tool_name_sent: bool = False
        self.streamed_args_for_tool: List[
            str
        ] = []  # map what has been streamed for each tool so far to a list
        self.bot_token = "[TOOL_CALLS]"
        self.bot_token_id = self.vocab.get(self.bot_token)
        self.tool_call_regex = re.compile(r"\[{.*?}\]", re.DOTALL)
        if self.bot_token_id is None:
            raise RuntimeError(
                "Mistral Tool Parser could not locate the tool call token in "
                "the tokenizer!")


    def extract_tool_calls(
        self,
        model_output: str,
        request: ChatCompletionRequest,
    ) -> ExtractedToolCallInformation:
        """
        Extract the tool calls from a complete model response. Requires
        find-and-replacing single quotes with double quotes for JSON parsing,
        make sure your tool call arguments don't ever include quotes!
        """
        # case -- if a tool call token is not present, return a text response
        if self.bot_token not in model_output:
            return ExtractedToolCallInformation(
                tools_called=False, tool_calls=[], content=model_output
            )
        try:
            # use a regex to find the tool call. remove the BOT token
            #   and make sure to replace single quotes with double quotes
            raw_tool_call = self.tool_call_regex.findall(
                model_output.replace(self.bot_token, "")
            )[0]
            # load the JSON, and then use it to build the Function and
            # Tool Call
            function_call_arr = json.loads(raw_tool_call)
            tool_calls: List[MistralToolCall] = [
                MistralToolCall(
                    type="function",
                    function=FunctionCall(
                        name=raw_function_call["name"],
                        # function call args are JSON but as a string
                        arguments=json.dumps(raw_function_call["arguments"]),
                    ),
                )
                for raw_function_call in function_call_arr
            ]
            # get any content before  the tool call
            content = model_output.split(self.bot_token)[0]
            return ExtractedToolCallInformation(
                tools_called=True,
                tool_calls=tool_calls,
                content=content if len(content) > 0 else None,
            )
        except Exception as e:
            logger.error(f"Error in extracting tool call from response: {e}")
            # return information to just treat the tool call as regular JSON
            return ExtractedToolCallInformation(
                tools_called=False, tool_calls=[], content=model_output
            )

    def extract_tool_calls_streaming(
        self,
        previous_text: str,
        current_text: str,
        delta_text: str,
        previous_token_ids: Sequence[int],
        current_token_ids: Sequence[int],
        delta_token_ids: Sequence[int],
        request: ChatCompletionRequest,
    ) -> Union[DeltaMessage, None]:
        # if the tool call token is not in the tokens generated so far, append
        # output to contents since it's not a tool
        if self.bot_token not in current_text:
            return DeltaMessage(content=delta_text)
        # if the tool call token ID IS in the tokens generated so far, that
        # means we're parsing as tool calls now
        # handle if we detected the BOT token which means the start of tool
        # calling
        if self.bot_token_id in delta_token_ids and len(delta_token_ids) == 1:
            # if it's the only token, return None, so we don't send a chat
            # completion any don't send a control token
            return None
        # bit mask flags for partial JSON parsing. If the name hasn't been
        # sent yet, don't allow sending
        # an incomplete string since OpenAI only ever (as far as I have
        # seen) allows sending the entire tool/ function name at once.
        flags = (
            Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
        )
        try:
            # replace BOT token with empty string, and convert single quotes
            # to double to allow parsing as JSON since mistral uses single
            # quotes instead of double for tool calls
            parsable_arr = current_text.split(self.bot_token)[-1]
            # tool calls are generated in an array, so do partial JSON
            # parsing on the entire array
            try:
                tool_call_arr: List[Dict] = partial_json_parser.loads(
                    parsable_arr, flags
                )
            except partial_json_parser.core.exceptions.MalformedJSON:
                logger.debug("not enough tokens to parse into JSON yet")
                return None
            # select as the current tool call the one we're on the state at
            current_tool_call: Dict = (
                tool_call_arr[self.current_tool_id]
                if len(tool_call_arr) > 0
                else {}
            )
            # case -- if no tokens have been streamed for the tool, e.g.
            #   only the array brackets, stream nothing
            if len(tool_call_arr) == 0:
                return None
            # case: we are starting a new tool in the array
            #   -> array has > 0 length AND length has moved past cursor
            elif (
                len(tool_call_arr) > 0
                and len(tool_call_arr) > self.current_tool_id + 1
            ):
                # if we're moving on to a new call, first make sure we
                # haven't missed anything in the previous one that was
                # auto-generated due to JSON completions, but wasn't
                # streamed to the client yet.
                if self.current_tool_id >= 0:
                    diff: Union[str, None] = current_tool_call.get("arguments")
                    if diff:
                        diff = json.dumps(diff).replace(
                            self.streamed_args_for_tool[self.current_tool_id],
                            "",
                        )
                        delta = DeltaMessage(
                            tool_calls=[
                                DeltaToolCall(
                                    index=self.current_tool_id,
                                    function=DeltaFunctionCall(
                                        arguments=diff
                                    ).model_dump(exclude_none=True),
                                )
                            ]
                        )
                        self.streamed_args_for_tool[
                            self.current_tool_id
                        ] += diff
                    else:
                        delta = None
                else:
                    delta = None
                # re-set stuff pertaining to progress in the current tool
                self.current_tool_id = len(tool_call_arr) - 1
                self.current_tool_name_sent = False
                self.streamed_args_for_tool.append("")
                logger.debug(f"starting on new tool {self.current_tool_id}")
                return delta
            # case: update an existing tool - this is handled below
            # if the current tool name hasn't been sent, send if available
            # - otherwise send nothing
            if not self.current_tool_name_sent:
                function_name = current_tool_call.get("name")
                if function_name:

                    delta = DeltaMessage(tool_calls=[
                        DeltaToolCall(index=self.current_tool_id,
                                      type="function",
                                      id=f"chatcmpl-tool-{random_uuid()}",
                                      function=DeltaFunctionCall(
                                          name=function_name).model_dump(
                                              exclude_none=True))
                    ])
                    self.current_tool_name_sent = True
                else:
                    delta = None
            # now we know we're on the same tool call and we're streaming
            # arguments
            else:
                prev_arguments = self.prev_tool_call_arr[
                    self.current_tool_id
                ].get("arguments")
                cur_arguments = current_tool_call.get("arguments")
                new_text = delta_text.replace("'", '"')
                if not cur_arguments and not prev_arguments:
                    delta = None
                elif not cur_arguments and prev_arguments:
                    logger.error(
                        "INVARIANT - impossible to have arguments reset "
                        "mid-arguments"
                    )
                    delta = None
                elif cur_arguments and not prev_arguments:
                    cur_arguments_json = json.dumps(cur_arguments)
                    logger.debug(
                        f"finding {new_text} in {cur_arguments_json}"
                    )
                    arguments_delta = cur_arguments_json[
                        : cur_arguments_json.index(new_text) + len(new_text)
                    ]
                    logger.debug(
                        f"First tokens in arguments received: {arguments_delta}"
                    )
                    delta = DeltaMessage(
                        tool_calls=[
                            DeltaToolCall(
                                index=self.current_tool_id,
                                function=DeltaFunctionCall(
                                    arguments=arguments_delta
                                ).model_dump(exclude_none=True),
                            )
                        ]
                    )
                    self.streamed_args_for_tool[
                        self.current_tool_id
                    ] += arguments_delta
                elif cur_arguments and prev_arguments:
                    cur_args_json = json.dumps(cur_arguments)
                    prev_args_json = json.dumps(prev_arguments)
                    logger.debug(
                        f"Searching for diff between \n{cur_args_json}\n"
                        f"{prev_args_json}"
                    )
                    argument_diff = extract_intermediate_diff(
                        cur_args_json, prev_args_json
                    )
                    logger.debug(f"got arguments diff: {argument_diff}")
                    delta = DeltaMessage(
                        tool_calls=[
                            DeltaToolCall(
                                index=self.current_tool_id,
                                function=DeltaFunctionCall(
                                    arguments=argument_diff
                                ).model_dump(exclude_none=True),
                            )
                        ]
                    )
                    self.streamed_args_for_tool[
                        self.current_tool_id
                    ] += argument_diff
                else:
                    # try parsing it with regular JSON - if it works we're
                    # at the end, and we need to send the difference between
                    # tokens streamed so far and the valid JSON
                    delta = None
            # check to see if the name is defined and has been sent. if so,
            # stream the name - otherwise keep waiting
            # finish by setting old and returning None as base case
            self.prev_tool_call_arr = tool_call_arr
            return delta
        except Exception as e:
            logger.error(f"Error trying to handle streaming tool call: {e}")
            logger.debug(
                "Skipping chunk as a result of tool streaming extraction "
                "error"
            )
            return None
